Evaluate Machine Learning Algorithm – leave one out cross validation in R Evaluating the performance of a machine learning algorithm is an important step in understanding how well it will work on new, unseen data. One popular method for evaluating the performance of an algorithm is called “leave-one-out cross validation” (LOOCV). In leave-one-out cross validation, …
Evaluate Machine Learning Algorithm in R – kfold cross validation in R Evaluating the performance of a machine learning algorithm is an important step in understanding how well it will work on new, unseen data. One popular method for evaluating the performance of an algorithm is called “k-fold cross validation.” In k-fold cross validation, the …
Evaluate Machine Learning Algorithm in R – dataset split in R Evaluating the performance of a machine learning algorithm is an important step in understanding how well it will work on new, unseen data. One common method for evaluating the performance of an algorithm is to split the available data into two sets: a training …
Machine Learning with CARET in R – Binary Classification with CARET in R CARET (short for “Classification And REgression Training”) is a powerful tool in R for training and comparing machine learning algorithms. One of the most common tasks in machine learning is binary classification, which is the process of sorting items into one of …
Algorithm Checkpoint with CARET in R CARET (short for “Classification and REgression Training”) is a powerful tool in R for training and comparing different machine learning algorithms. It provides a consistent and easy-to-use interface for working with many different algorithms, including decision trees, random forests, support vector machines (SVMs), and more. One of the most …
Classification in R – SVM in R Support Vector Machine (SVM) is a popular method for classification in machine learning and data analysis. It’s a type of algorithm that can sort items into different categories based on their characteristics. An SVM works by finding the best boundary, or “decision boundary,” that separates the different classes …
Classification in R – random forest in R Classification is a way of sorting items into different categories. It’s a common task in machine learning and data analysis. One popular method for classification is called “random forest,” which is a type of decision tree algorithm. A decision tree is a flowchart-like structure that breaks down …
Classification in R – partial least squares discriminant in R Classification is a type of supervised machine learning that is used to predict the class or category of a new observation based on the values of its predictors. One popular method of classification is using partial least squares discriminant analysis (PLS-DA). PLS-DA is a technique …
Classification in R – naive bayes in R Classification is a type of supervised machine learning that is used to predict the class or category of a new observation based on the values of its predictors. One popular method of classification is using naive bayes algorithm. Naive Bayes is a probabilistic algorithm that is based …
Classification in R – logistic regression for multiclass classification in R Classification is a type of supervised machine learning that is used to predict the class or category of a new observation based on the values of its predictors. One popular method of classification is using logistic regression for multiclass classification. Multiclass classification is a …